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Description
Efficient management and retrieval of scientific information are crucial in the era of big data and machine learning. This study presents a prototype of a recommendation system that helps researchers select the most suitable journal for publishing their scientific articles. The system utilizes metadata and keyword filtering techniques to retrieve relevant information from open APIs. By analyzing factors such as citation counts, publication dates, and keywords, the system compiles a thematic list of significant scientific sources. This comprehensive list aids researchers in knowledge exploration and research direction identification.
Additionally, the system generates visualizations that provide researchers with insights into the distribution of scientific articles, popular keywords, and overall trends. These visualizations offer valuable information for exploring related papers, identifying influential authors, and discovering emerging trends in the field. While visualizations are not interactive, they improve understanding of the research area and facilitate informed decision-making.
Compared to existing systems the created prototype offers some advantages, in particular it leverages advanced machine learning algorithms for accurate and personalized journal recommendations.
In summary, the prototype recommendation system improves scientific information retrieval. By considering various factors and utilizing advanced algorithms, it assists researchers in selecting suitable journals, maximizing the impact and visibility of their work within the scientific community.
